Solved and Unsolved Problems in
Chemoinformatics
Johann Gasteiger
Computer-Chemie-Centrum
University of Erlangen-Nürnberg
D-91052 Erlangen, Germany
Johann.Gasteiger@fau.deC3 © Gasteiger

Overview• objectives of lecture• achievements• challenges• unsolved problems• summaryC3 © Gasteiger

Objectives• many problems have been solved

let us be proud!

•there is still a lot to be done

chemoinformatics is a field of its own,
is attractive for students

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Chemoinformatics – An Old Discipline• Structure activity relationships
1963 Hantsch & Fujita• structure representation
1965, Morgan• structure elucidation
1965, Sasaki, Munk, DENDRAL• synthesis design
1970, Corey & Wipke, Ugi+Gasteiger, Hendrickson• molecular modeling
1970, Langridge, Marshall• data analysis / chemometrics
1970, Kowalski, Wold

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Achievements• access to chemical information• learning from chemical information• applications

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Databases• Chemical Abstracts Service• Cambridge Structure Database• Beilstein• Gmelin• ChemInformRX• SpecInfo• PubChem• etc.

C3 © Gasteiger(1975)(1984)(1990)(1990)(1991)(1991)(2004)

x

Number of Compounds in Chemistrycompounds published in CAS

102 million
compounds,
66 million
sequences(June 2015)

compounds (millions)

50
40
30
20
10
0
1965

1970

1975

1980

1985

year

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1990

1995

2000

2005

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Search for Cancerostatic Drugs

protein/substrate complex

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similar substrates

Chemical Structures• computers have learnt the language of a chemist

communicating by structure diagrams•molecules are stored with atomic resolution providing
access to each atom and bondenabling substructure search•the complex structures of molecules can be visualized

new insights can be gainedC3 © Gasteiger

Summary• databases have strongly contributed to theprogress in chemistry and related fields• without databases modern research inchemistry would be inconceivableC3 © Gasteiger

Learning from Chemical Information• learning from data
• QSAR/QSPR• representation of chemical structures

C3 © Gasteiger

Problem: Not Enough Information
102,000,000

1,000,000

chemical compounds

3D structures in
Cambridge Crystallographic Data File

we only have data on the 3D structure for less than
1% of the known compounds

C3 © Gasteiger

Problem: Not Enough Information
102,000,000

1,000,000

chemical compounds

3D structures in
Cambridge Crystallographic Data File

we only have data on the 3D structure for less than
1% of the known compounds
can we learn the rules from the known 3D structures

C3 © Gasteiger

Rule-Based Learning: CORINA
connection table & stereo descriptors
initialization ofinternal coordinates*

rings:
ring perception
ring template searchring assembly
acyclic systems:
removal of steric crowding
3D coordinates

generates 3D coordinates for >99.5 % of all organic compounds
J.Sadowski, J.Gasteiger, Chem.Reviews, 1993, 93, 2567-2581.
http://www.molecular-networks.com/products/corina/

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Data-Based Learning
Structure-Property Relationships (QSPR, QSAR)

molecular
structure//

representation

model building

structuredescriptors

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propertyRepresentation of Chemical Structures• topological indices• fragment codes• fingerprints• ….
Several thousands of different types ofchemical descriptors have been developed
R.Todeschini, V.Consonni, Molecular Descriptors in
Chemoinformatics, 2 volumes, Wiley-VCH, 2009

C3 © Gasteiger

Representation of Chemical Structures
O
N

O
+ NH

3

constitutionNH

3D modelmolecular
surface
Of Molecules and Humans
J. Gasteiger,
J. Med. Chem., 2006, 55, 6429 - 6434

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Prediction of Properties• physical, e.g.
– aqueous solubility– 13C NMR shifts• chemical, e.g.
– acidity• biological, e.g.
– toxicity

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Applications of Chemoinformatics: Drug Design
Chemoinformatics has become an integral part of the
drug design process• lead discovery• virtual screening• pharmacophore searching• lead optimization• QSAR• molecular docking• prediction of ADME properties• solubility, adsorption, distribution, metabolism, excretion….

C3 © Gasteiger

Handbook of
Chemoinformatics
From Data to Knowledge
J. Gasteiger (Editor)
65 authors
73 contributions
4 volumes
1900 pages
Wiley-VCH, Weinheim(August 2003)

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Chemoinformatics- A Textbook J. Gasteiger, T. Engel(Editors)
650 pages
Wiley-VCH, Weinheim(September 2003)

C3 © Gasteiger

Challenges• essense of chemistry• environmental impact of chemicals• understanding chemistry• understanding biological systems• human healthapplications in all fields of chemistry and relatedscientific disciplines

C3 © Gasteiger

Essense of Chemistry
Chemical Industry is not sellingcompounds
but is selling
properties
i.e., compounds with desired properties(drugs, paints, fibers, plastics, pesticides, catalysts,
ceramics, etc.)

C3 © Gasteiger

Fundamental Questions in Chemistry
What structure do I need for a certain property?
structure-activity relationships
How do I make this structure?
synthesis design
What is the product of my reaction?
reaction prediction
structure elucidation
In all those areas the use of chemoinformatics could help!

C3 © Gasteiger

Risk Assessment of Chemicals
REACH – Registration, Evaluation, Authorization andrestriction of Chemicals• for those chemicals used with more than 10 tons/year manufactured orimported into the European Union a Chemical Safety Report is needed• law since June 1, 2007; registration until Dec 1, 2013
• applies to about 35,000 chemicals• testing on harmful effects on human health or environment, determination of
persistence, bioaccumulation and toxicity• testing is time-consuming, expensive and might need many animals

Use chemoinformatics methods for ranking of chemicals

C3 © Gasteiger

Cosmetics Directive• for chemicals used in cosmetics products• no compounds tested on animals are allowed in cosmetics
in Europe since 2009.
• all animal tested cosmetics will eventually be banned on theEuropean market.

Use chemoinformatics methods for developing
alternatives to animal testingC3 © Gasteiger

Problems Still to be Solved• access to chemical information• learning from chemical information• applications

C3 © Gasteiger

Improved Access to Chemical Information• input of chemical structures (hand writing, voice)• representation of neglected compounds (boranes, organometallic structures (ferrocene, etc.), Markush structures (patents),
polymers)• text mining (optical character recognition)• publishing chemical information (3D structures, spectra)• publishing and searching on the internet

C3 © Gasteiger

Neede for Better Databases• on compounds• store all available information (all properties)• store all spectra• on reactions• give the entire stoichiometry of a reaction• store all reaction conditions (solvent, temperature,reaction time)• kinetic dataC3 © Gasteiger

From Experiment to Reaction Database(Presently)
information
producer

experimentreportlab journal

writtenonpaper

manuscript
many breaks
ininformation
processing

patentpublicationelectronicallysecondaryliterature

abstract
input into
reaction database

information
consumerC3 © Gasteiger

printedreaction
schemereaction
databaseFrom Experiment to Reaction Database(Future: Electronic Lab Notebooks)
experiment
information
producer
reporte lab notebook

writtenX on
papermanuscript
a direct
information
flow

patentpublicationelectronicallysecondaryliterature

abstract
input into
reaction database

information
consumerC3 © Gasteiger

printedreaction
schemereaction
databaseLearning from Chemical Information• Note: standard QSAR studies have difficulties to get
published if they do not also
- provide new experimental results or- increase our knowledge and insight• represent structures by descriptors that can be interpreted• combine substructures with physicochemical effects• use data analysis methods that are not a black box• a forest of decision trees gives more exact predictions but cannot beinterpreted; a single decision tree can be interpreted!

• combine a QSAR model with e.g. docking studies
From models to interpretation

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Applications• all fields of chemistry• drug design• analytical chemistry• organic chemistry• biochemistry• toxicology• material scienceC3 © Gasteiger

Drug Design• conformational flexibility of drugs and proteins• docking into proteins• no consensus scoring (science cannot be predicted by voting)• try to model the physicochemistry of the process• protein-protein and protein-DNA interactions• prediction of ADME-Tox properties• model the various organs of a humanC3 © Gasteiger

Analytical Chemistry• origin of olive oils

• simulation of infrared spectra• 3D structure from infrared spectrum

C3 © Gasteiger

Italian Olive Oils
9 regions572 olive oil samples
8

7

1
3
9

palmitic acid
palmitoleic acidstearic acidoleic acid5
6

contents of 8 fatty acids:4

linoleic acidarachidic acidlinoleic acideicosenoic acid2

M.Forina, et al. Ann.Chim.(Rome), 1982, 72, 127 and 144.

C3 © GasteigerSelf-Organizing Map of Olive Oils
6 66 66 55 55 55 8 8 88 88 8
6 66 65 5 55 5 7 8 88 8
6 66 6 55 55 55
7 8 88 88
6 66 65 5 55 55 7 7 77 88 88
66 55 55 7 7 7 77 7 88
33
55 5 77 7 9 99 9 83
55 5 77 7 9 99 933
5 5 77 77 9 99 99
33
3
7 77 7 9 99
1
3 33 33 33
77 79
1 41
3 33 33 33 22
4 2 11 11
3 33 3 33 33 2 22 2 22 1 11
3 33 33 33 33
22
1 11
3 3
33 3
3 22 2 2 2 44
3 33 33 33 33 33 2 2 22
3 33 33 33 33 32 22 4 44
3 33 3 33 33 33 33 4 22 44 44
3 33 3 3 33 33 4 2
2 22
3 3 3 33 33 3 44 3 44 22 22
3 33 33 33 33 33 3 4 44 22 24

572 samples
9 regions
8 fatty acids

250 training
322 test
10 prediction errors

J.Zupan, M.Novic, X.Li, J. Gasteiger, Anal. Chim. Acta, 1994, 292, 219-234

C3 © GasteigerSelf-Organizing Map of Olive Oils
6 66 66 55 55 55 8 8 88 88 8
6 66 65 5 55 5 7 8 88 8
6 66 6 55 55 55
7 8 88 88
6 66 65 5 55 55 7 7 77 88 88
66 55 55 7 7 7 77 7 88
33
55 5 77 7 9 99 9 83
55 5 77 7 9 99 933
5 5 77 77 9 99 99
33
3
7 77 7 9 99
1
3 33 33 33
77 79
1 41
3 33 33 33 22
4 2 11 11
3 33 3 33 33 2 22 2 22 1 11
3 33 33 33 33
22
1 11
3 3
33 3
3 22 2 2 2 44
3 33 33 33 33 33 2 2 22
3 33 33 33 33 32 22 4 44
3 33 3 33 33 33 33 4 22 44 44
3 33 3 3 33 33 4 2
2 22
3 3 3 33 33 3 44 3 44 22 22
3 33 33 33 33 33 3 4 44 22 24

1

8 7

3

The merits ofunsupervised
learning

9
5
6

4
2

J.Zupan, M.Novic, X.Li, J. Gasteiger, Anal. Chim. Acta, 1994, 292, 219-234

C3 © Gasteiger

Computer-Assisted Structure Elucidation (CASE)• CASE was one of the roots of chemoinformatics• various groups worked on it (Munk, Sasaki, Funatsu,
Steinrück)• not much done recently• no useful general purpose system available• much time spent by chemists on structure elucidation
structure elucidation should be done more efficiently,
using all available information and using software

C3 © Gasteiger

Simulation of Infrared Spectra
Predict the Infrared Spectrum from the structure of a compound
3D structure representation by radial distribution function
N 1 Ng (r )    Ai A j e
i 1

N
A
B
rij

C3 © Gasteiger B ( r  rij ) 2

j inumber of atoms
atomic properties
temperature or smoothing factor
interatomic distanceTraining of a Counterpropagation (CPG) Network

3D structuretransformation

Radialcode(128 values)

IR spectrum(128 absorbance values)

C3 © Gasteiger

Simulation of IR Spectra
0.8

absorbanceO

simulation
experimentOr = 0.965
0.4
Br

0
3500

3000

2500

2000

1500

J.Schuur, J.Gasteiger, Anal.Chim. 1997, 69, 2398-2405.

C3 © Gasteiger

1000

cm-1
500

3D Structure Prediction from IR Spectrum
decoding

3D structure

Radialcode(128 values)

IR spectrum(128 absorbance values)

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IR Spectrum of Unknown Compound
0.4
Abs.

0.3

0.2

0.1

0
3500

C3 © Gasteiger

3000

2500

2000

1500

1000

-1

cm

500

3D Structure Prediction from IR Spectra

O
N
O
O

Correct prediction!

F

M.Hemmer, J.Gasteiger, Anal.Chim.Acta, 2000, 420, 145-154.

C3 © Gasteiger

Organic Chemistry• chemical reactivity• organic synthesis design

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Chemical Reactivity• needs better data in reaction databases• reach out to theoretical chemists• put the results of quantum mechanical calculations
into databases

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Calculation of Chemical Effects
charge distribution
J. Gasteiger, M. Marsili, Tetrahedron 36, 3219 (1980)
inductive effect
J. Gasteiger, M. G. Hutchings, Tetrah. Lett. 24, 2541 (1983)
resonance effect
J. Gasteiger, H. Saller, Angew. Chem. Int. Ed. Engl. 24, 687 (1985)
polarizability effect
J. Gasteiger, M. G. Hutchings, J. Chem. Soc. Perkin 2, 559 (1984)
bond dissociation energy
J. Gasteiger, Comp. Chem. 2, 85 (1978)

C3 © Gasteiger

Calculation of Proton Affinities (PA)
N +H

N

H

• alkyl amines only (49 cpds)
PA(kJ/mol) = 205.6 + 2.82 ad,N
J. Gasteiger, M.G. Hutchings, J. Chem. Soc. Perkin 2, 1984, 559• alkyl amines + heteroatom substituted alkyl amines (80 cpds)
PA(kJ/mol) = 1435.5 + 12.5 ad,N - 116.3 cr,N

J. Gasteiger, M.G. Hutchings, Tetrahedron Lett. 1983, 2541

C3 © Gasteiger

A pKa Model of Aliphatic Carboxylic Acids
R1
R2

C
c

R1

O
+ H2 O

C

H

O

R3

Q

A2D

R2

C
R3

O
+ H O
3

C

O

a

pK a  37.54Q ,O  12.27 A2 D,O  1.02a O  0.11c ,a C  1.89I amino  19.10
n = 1122, r2 = 0.81, s = 0.42, F = 809

J.Zhang, T.Kleinoeder, J.Gasteiger, J.Chem.Inf.Model., 2006, 46, 2256-2266.

C3 © Gasteiger

Computer-Assisted Organic Synthesis Design (CASD)• CASD was one of the roots of chemoinformatics• many products can be traced back to CASD work
•MACCS, REACCS, Beilstein DB, ChemInform RX DB• however CASD systems are not yet widely acceptedby organic chemists• but it is still true:“The amount of information to be processed and the decisions between
many alternatives suggests the use of computers in synthesis design.”(H.Gelernter, 1973)

The design of organic syntheses should be done more
efficiently, using all available information, by using software

C3 © Gasteiger

Biochemistry
andBioinformatics• Study of diseases

C3 © Gasteiger

C3 © Gasteiger

Application of BioPath.Database• search for enzyme inhibitors
M.Reitz, A.von Homeyer, J.Gasteiger,
J.Chem.Inf.Model., 2006, 46, 2324-2332• search for similar enzymes
O.Sacher, M.Reitz, J.Gasteiger,
J.Chem.Inf.Model., 2009, 49, 1525-1534
X.Hu, A,Yan, T.Tan, O.Sacher, J.Gasteiger
J.Chem.Inf.Model., 2010, 50, 1089-1100• discover essential pathways of diseases
G.Kastenmüller, J.Gasteiger, H.W.Mewes,
Bioinformatics, 2008, 24, i56-i62
G.Kastenmüller, M.E.Schenk, J.Gasteiger, H.W.Mewes,
Genome Biology, 2009,10, R28
http://www.molecular-networks.com/databases/biopathC3 © Gasteiger

Uncovering Metabolic Pathways to Phenotypic Traits
PEDANT
Annotatedgenomes

BioPathdatabase
Metabolicreconstruction
Phenotypic
traitsPathway profile

Attribute selection+ Pathway ranking

Relevant pathways

C3 © Gasteiger

56

Periodontal Disease
Human oral flora:> 700 species

PEDANT:
15 fully sequenced oral genomes(incl. 4 of 6 periodontal pathogens)

BioPath
68 global pathways
306 smaller pathways

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57

Relevant Pathways for Phenotype
Periodontal Disease Causing
Glutamate fermentation
Biosynthesis of L-proline
Biosynthesis of 5-formimino-THF
Conversion of l-glutamate to L-proline
Conversion of l-glutamate to l-ornithine
Degradation of l-histidine to l-glutamate

G. Kastenmüller, M.E. Schenk, J. Gasteiger, H.W.Mewes,
Genome Biology, 2009,10, R28

C3 © Gasteiger

58Relevant Pathways for Phenotype
Periodontal Disease Causing
Glutamate fermentation
Biosynthesis of L-proline
Biosynthesis of 5-formimino-THF
Conversion of l-glutamate to L-proline
Conversion of l-glutamate to l-ornithine
Degradation of l-histidine to l-glutamate
Several of these pathways produce NH3
Cytotoxic NH3 plays a major role in periodontal disease
G. Kastenmüller, M.E. Schenk, J. Gasteiger, H.W.Mewes,
Genome Biology, 2009,10, R28

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59

Bioinformatics

geneC3 © Gasteiger

proteinChemoinformatics

drug

lead

Toxicity and Risk Assessment• meeting the challenges posed by REACH, drug design

and Cosmetics Directive• projects funded by the European Union, Innovative
Medicine Initiative and Cosmetics Europe• eTOX (11 academic groups, 6 SMEs, 13 pharma companies)• COSMOS (5 academic groups, 3 public institutions, 5 SMEs,
2 cosmetics companies)

C3 © Gasteiger

Search Structures for Toxic Alerts• Chemotypes> structure fragments with physicochemical properties• ToxPrint> alerting chemotypes for genotoxic
carcinogens• Publicly available• www.chemotyper.org• www.toxprint.org
CSRML: C.Yang et al., J. Chem. Inf. Model., 2015, 55, 510-528

C3 © Gasteiger

Material Sciences• Diverse material properties

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QSPR Modeling of Diverse Material Properties
David A. Winkler, CSIRO, Melbourne, Australia• Nanomaterials• Catalysts• Biomaterials• Polymers• Ionic liquids• Supercritical carbon dioxide• Ceramics
T.Le, V.C.Epa, F.R.Burden, D.A.Winkler, Chem.Reviews, 2012, 112, 2889-2919.

C3 © Gasteiger

Conclusions• Chemoinformatics has become indispensable indrug design• has many potential applications in all areas of chemistry
and other scientific fields• talk to colleagues about their problems• build models with their data – and knowledge• Society expects a lot from chemoinformatics(Nobel Prize in Chemistry 2013: Karplus, Levitt, Warshel)C3 © Gasteiger

Teaching• several textbooks have been published• Gillet+Leach, Gasteiger+Engel, Bajorath, Wild

• define curriculum in chemoinformatics• various universities already teach chemoinformatics• the number is growing• integrate chemoinformatics into regularchemistry curricula

C3 © Gasteiger

Acknowledgements• Many excellent and dedicated coworkers at
Computer-Chemie-Centrum, University of Erlangen-Nuremberg,
Germany
http://www2.chemie.uni-erlangen.de• Excellent coworkers at

http://www.molecular-networks.com• Dr.Ulrike Gasteiger

C3 © Gasteiger

C3 © Gasteiger

Atorvastatin
O
O
O

N

OH

3D structure

CORINAN
H
F

graphic
file

C3 © Gasteiger

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